docs: def terms(model, threshold=0.01, **kwargs):
      """
      Two terms are coupled if the posterior probability for both terms is
      greather than ``threshold`` for the same topic.
  
      Parameters
      ----------
      model : :class:`.LDAModel`
      threshold : float
          Default: 0.01
      kwargs : kwargs
          Passed on to :func:`.cooccurrence`\.
  
      Returns
      -------
      :ref:`networkx.Graph <networkx:graph>`
  
      """
  
      select = lambda f, v, c, dc: v > threshold
      graph = cooccurrence(model.phi, filter=select, **kwargs)
  
      # Only include labels for terms that are actually in the graph.
      label_map = {k: v for k, v in model.vocabulary.items()
                   if k in graph.nodes()}
      graph.name = ''
      return networkx.relabel_nodes(graph, label_map)